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Course Outline
Introduction
Setting up the R Development Environment
Deep Learning vs Neural Network vs Machine Learning
Building an Unsupervised Learning Model
Case Study: Predicting an Outcome Using Existing Data
Preparing Test and Training Data Sets For Analysis
Clustering Data
Classifying Data
Visualizing Data
Evaluating the Performance of a Model
Iterating Through Model Parameters
Hyper-parameter Tuning
Integrating a Model with a Real-World Application
Deploying a Machine Learning Application
Troubleshooting
Summary and Conclusion
Requirements
- R programming experience
- An understanding of machine learning concepts
21 Hours
Testimonials (2)
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
